Influence of Natural Organic Matter Source on Copper Speciation As Demonstrated by Cu Binding to Fish Gills, by Ion Selective Electrode, and by DGT Gel Sampler
Bibliographic record
Abstract
Rainbow trout (Oncorhynchus mykiss, 2 g) were exposed to 0-5 microM total copper in ion-poor water for 3 h in the presence or absence of 10 mg C/L of qualitatively different natural organic matter (NOM) derived from water spanning a large gradient in hydrologic residence time. Accumulation of Cu by trout gills was compared to Cu speciation determined by ion selective electrode (ISE) and by diffusive gradients in thin films (DGT) gel sampler technology. The presence of NOM decreased Cu uptake by trout gills as well as Cu concentrations determined by ISE and DGT. Furthermore, the source of NOM influenced Cu binding by trout gills with high-color, allochthonous NOM decreasing Cu accumulation by the gills more than low-color autochthonous NOM. The pattern of Cu binding to the NOM measured by Cu ISE and by Cu accumulation by DGT samplers was similar to the fish gill results. A simple Cu-gill binding model required an NOM Cu-binding factor (F) that depended on NOM quality to account for observed Cu accumulation by trout gills; values of Fvaried by a factor of 2. Thus, NOM metal-binding quality, as well as NOM quantity, are both important when assessing the bioavailability of metals such as Cu to aquatic organisms.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".